In this paper, a novel method is proposed to improve ELM representative and interpretative ability by extraction diversified top-k shapelets features.Shapelets was introduced as a primitive for time series data mining [1]} and was utilized in classifying time series data [2]}, [3]}. The original shapelet based classifier embeds the shapelet discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates. Shapelets transformed classification methods [4]}, [5]} were proposed to separate the processing of shapelets selection and classification. The k shapelets are selected in an offline manner, and which can not only improve the affectivity and efficiency of classification, but also introduce a common feature attraction method which can be used in all typical time series classification algorithms. Nevertheless, shapelets based classification methods have been widely discussed and used in many real applications [6]}, [7]}, [8]}.
